图神经网络(GNN)必读论文及最新进展跟踪

https://www.toutiao.com/a6703123171454747149/

 

 

2019-06-16 21:41:22

2018年AI领域最闪耀的技术，除了NLP领域以Bert、GPT模型等为代表的无监督预训练技术之外，另外一个研究热点就是Graph Neural Network（GNN），并且这一热点在2019年还会继续持续。本文以GNN为重点，列出相关必读论文，并跟踪技术最新进展情况。我们期待着推动这一方向技术进步，并向这一方向的研究人员提供一些帮助。

本文内容整理自网络，原文地址：https://github.com/jdlc105/Must-read-papers-and-continuous-tracking-on-Graph-Neural-Network-GNN-progress

现实世界中的很多问题和应用都可以图的形式来表示，例如社交网络、蛋白质相互作用网络、大脑网络、化学分子图和3D点云。因此，在跨学科研究的推动下，面向图形数据的神经网络模型已经成为一个新兴的研究热点。其中，深度学习的三位先驱中的两位，Yann LeCun教授(2018年图灵奖获得者)、Yoshua Bengio教授(2018年图灵奖获得者)和斯坦福大学人工智能实验室著名的Jure Leskovec教授也加入到这个领域研究之中。

技术关键词

Graph Neural Network, Graph convolutional network, Graph network, Graph attention network, Graph auto-encoder

当前热门的研究课题：由 T.N. Kipf和M. Welling在ICLR2017中提出的代表性工作—图卷积网络(GCNs)，在Google Scholar(截至2019年5月9日)中被引用了1020次。更新:1065次(截至2019年5月20日)。更新:1106次(截至2019年5月27日)。

项目开始时间:2018年12月11日，最新更新时间:2019年5月27日

更多关于GCN模型及其应用的论文将来自CVPR 2019、WWW2019、SIGKDD2019、ICML2019....坐等这些论文Release出来。

综述论文

1、Ziwei Zhang, Peng Cui, Wenwu Zhu, Deep Learning on Graphs: A Survey, ArXiv, 2018.

 

由清华大学校崔鹏老师等整理的深度学习图技术分类论文。

2、Jie Zhou, Ganqu Cui, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, Maosong Sun, Graph Neural Networks: A Review of Methods and Applications, ArXiv, 2018.

 

来自清华大学刘洋老师团队的综述论文。

3、Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, Philip S. Yu(Fellow,IEEE), A Comprehensive Survey on Graph Neural Networks, ArXiv, 2019.

 

期刊论文

F. Scarselli, M. Gori, A.C. Tsoi, M. Hagenbuchner, G. Monfardini, The graph neural network model, IEEE Transactions on Neural Networks(IEEE Transactions on Neural Networks and Learning Systems), 2009. paper.

Scarselli F, Gori M, Tsoi A C, et al. Computational capabilities of graph neural networks, IEEE Transactions on Neural Networks, 2009. paper.

Micheli A . Neural Network for Graphs: A Contextual Constructive Approach. IEEE Transactions on Neural Networks, 2009. paper.

Goles, Eric, and Gonzalo A. Ruz. Dynamics of Neural Networks over Undirected Graphs. Neural Networks, 2015. paper.

Z. Luo, L. Liu, J. Yin, Y. Li, Z. Wu, Deep Learning of Graphs with Ngram Convolutional Neural Networks, IEEE Transactions on Knowledge & Data Engineering, 2017. paper. code.

Petroski Such F , Sah S , Dominguez M A , et al. Robust Spatial Filtering with Graph Convolutional Neural Networks. IEEE Journal of Selected Topics in Signal Processing, 2017. paper.

Kawahara J, Brown C J, Miller S P, et al. BrainNetCNN: convolutional neural networks for brain networks; towards predicting neurodevelopment. NeuroImage, 2017. paper.

Muscoloni A , Thomas J M , Ciucci S , et al. Machine learning meets complex networks via coalescent embedding in the hyperbolic space. Nature Communications, 2017. paper.

D.M. Camacho, K.M. Collins, R.K. Powers, J.C. Costello, J.J. Collins, Next-Generation Machine Learning for Biological Networks, Cell, 2018. paper.

Marinka Z , Monica A , Jure L . Modeling polypharmacy side effects with graph convolutional networks. Bioinformatics, 2018. paper.

Sarah P , Ira K S , Enzo F , et al. Disease Prediction using Graph Convolutional Networks: Application to Autism Spectrum Disorder and Alzheimer’s Disease. Medical Image Analysis, 2018. paper.

Sofia Ira Ktena, Sarah Parisot, Enzo Ferrante, Martin Rajchl, Matthew Lee, Ben Glocker, Daniel Rueckert, Metric learning with spectral graph convolutions on brain connectivity networks, NeuroImage, 2018. paper.

Xie T , Grossman J C . Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties. Physical Review Letters, 2018. paper.

Phan, Anh Viet, Minh Le Nguyen, Yen Lam Hoang Nguyen, and Lam Thu Bui. DGCNN: A Convolutional Neural Network over Large-Scale Labeled Graphs. Neural Networks, 2018. paper

Song T, Zheng W, Song P, et al. Eeg emotion recognition using dynamical graph convolutional neural networks. IEEE Transactions on Affective Computing, 2018. paper

Levie R, Monti F, Bresson X, et al. Cayleynets: Graph convolutional neural networks with complex rational spectral filters. IEEE Transactions on Signal Processing 2019. paper

Zhang, Zhihong, Dongdong Chen, Jianjia Wang, Lu Bai, and Edwin R. Hancock. Quantum-Based Subgraph Convolutional Neural Networks. Pattern Recognition, 2019. paper

Qin A, Shang Z, Tian J, et al. Spectral–Spatial Graph Convolutional Networks for Semisupervised Hyperspectral Image Classification. IEEE Geoscience and Remote Sensing Letters, 2019. paper

Coley C W, Jin W, Rogers L, et al. A graph-convolutional neural network model for the prediction of chemical reactivity. Chemical Science, 2019. paper

Zhang Z, Chen D, Wang Z, et al. Depth-based Subgraph Convolutional Auto-Encoder for Network Representation Learning. Pattern Recognition, 2019. paper

Hong Y, Kim J, Chen G, et al. Longitudinal Prediction of Infant Diffusion MRI Data via Graph Convolutional Adversarial Networks. IEEE transactions on medical imaging, 2019. paper

Khodayar M, Mohammadi S, Khodayar M E, et al. Convolutional Graph Autoencoder: A Generative Deep Neural Network for Probabilistic Spatio-temporal Solar Irradiance Forecasting. IEEE Transactions on Sustainable Energy, 2019. paper

会议论文

Duvenaud D, Maclaurin D, Aguilera-Iparraguirre J, et al. Convolutional networks on graphs for learning molecular fingerprints, NeurIPS(NIPS) 2015. paper. code.

M. Niepert, M. Ahmed, K. Kutzkov, Learning Convolutional Neural Networks for Graphs, ICML 2016. paper.

S. Cao, W. Lu, Q. Xu, Deep neural networks for learning graph representations, AAAI 2016. paper.

M. Defferrard, X. Bresson, P. Vandergheynst, Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering, NeurIPS(NIPS) 2016. paper. code.

T.N. Kipf, M. Welling, Semi-Supervised Classification with Graph Convolutional Networks, ICLR 2017. paper. code.

A. Fout, B. Shariat, J. Byrd, A. Benhur, Protein Interface Prediction using Graph Convolutional Networks, NeurIPS(NIPS) 2017. paper.

Monti F, Bronstein M, Bresson X. Geometric matrix completion with recurrent multi-graph neural networks, NeurIPS(NIPS) 2017. paper.

Simonovsky M, Komodakis N. Dynamic edgeconditioned filters in convolutional neural networks on graphs, CVPR. 2017. paper

R. Li, S. Wang, F. Zhu, J. Huang, Adaptive Graph Convolutional Neural Networks, AAAI 2018. paper

J. You, B. Liu, R. Ying, V. Pande, J. Leskovec, Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation, NeurIPS(NIPS) 2018. paper.

C. Zhuang, Q. Ma, Dual Graph Convolutional Networks for Graph-Based Semi-Supervised Classification, WWW 2018. paper

H. Gao, Z. Wang, S. Ji, Large-Scale Learnable Graph Convolutional Networks, KDD 2018. paper

D. Zügner, A. Akbarnejad, S. Günnemann, Adversarial Attacks on Neural Networks for Graph Data, KDD 2018. paper

Ying R , He R , Chen K , et al. Graph Convolutional Neural Networks for Web-Scale Recommender Systems. KDD 2018. paper

P. Veličković, G. Cucurull, A. Casanova, A. Romero, P. Liò, Y. Bengio, Graph Attention Networks, ICLR, 2018. paper

Beck, Daniel Edward Robert, Gholamreza Haffari and Trevor Cohn. Graph-to-Sequence Learning using Gated Graph Neural Networks. ACL 2018. paper

Marcheggiani D , Bastings J , Titov I . Exploiting Semantics in Neural Machine Translation with Graph Convolutional Networks. NAACL 2018. paper

Chen J , Zhu J , Song L . Stochastic Training of Graph Convolutional Networks with Variance Reduction. ICML 2018. paper

Gusi Te, Wei Hu, Amin Zheng, Zongming Guo, RGCNN: Regularized Graph CNN for Point Cloud Segmentation. ACM Multimedia 2018. paper, code,

Talukdar, Partha, Shikhar Vashishth, Shib Sankar Dasgupta and Swayambhu Nath Ray. Dating Documents using Graph Convolution Networks. ACL 2018. paper, code

Sanchez-Gonzalez A , Heess N , Springenberg J T , et al. Graph networks as learnable physics engines for inference and control. ICML 2018. paper

Muhan Zhang, Yixin Chen. Link Prediction Based on Graph Neural Networks. NeurIPS(NIPS) 2018. paper

Chen, Jie, Tengfei Ma, and Cao Xiao. FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling. ICLR 2018. paper

Zhang, Zhen, Hongxia Yang, Jiajun Bu, Sheng Zhou, Pinggang Yu, Jianwei Zhang, Martin Ester, and Can Wang. ANRL: Attributed Network Representation Learning via Deep Neural Networks.. IJCAI 2018. paper

Rahimi A , Cohn T , Baldwin T . Semi-supervised User Geolocation via Graph Convolutional Networks. ACL 2018. paper

Morris C , Ritzert M , Fey M , et al.Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks.. AAAI 2019. paper

Xu K, Hu W, Leskovec J, et al. How Powerful are Graph Neural Networks?, ICLR 2019. paper

Johannes Klicpera, Aleksandar Bojchevski, Stephan Günnemann. Combining Neural Networks with Personalized PageRank for Classification on Graphs, ICLR 2019. paper

Daniel Zügner, Stephan Günnemann. Adversarial Attacks on Graph Neural Networks via Meta Learning, ICLR 2019. paper

Zhang Xinyi, Lihui Chen. Capsule Graph Neural Network, ICLR 2019. paper

Liao, R., Zhao, Z., Urtasun, R., and Zemel, R. LanczosNet: Multi-Scale Deep Graph Convolutional Networks, ICLR 2019, paper

Bingbing Xu, Huawei Shen, Qi Cao, Yunqi Qiu, Xueqi Cheng. Graph Wavelet Neural Network, ICLR 2019, paper

Hu J, Guo C, Yang B, et al. Stochastic Weight Completion for Road Networks using Graph Convolutional Networks ICDE. 2019. paper

Yao L, Mao C, Luo Y . Graph Convolutional Networks for Text Classification. AAAI 2019. paper

Landrieu L , Boussaha M . Point Cloud Oversegmentation with Graph-Structured Deep Metric Learning. CVPR 2019. paper

Si C , Chen W , Wang W , et al. An Attention Enhanced Graph Convolutional LSTM Network for Skeleton-Based Action Recognition. CVPR 2019. paper

Cucurull G , Taslakian P , Vazquez D . Context-Aware Visual Compatibility Prediction. CVPR 2019. paper

Jia-Xing Zhong, Nannan Li, Weijie Kong, Shan Liu, Thomas H. Li, Ge Li. Graph Convolutional Label Noise Cleaner: Train a Plug-and-play Action Classifier for Anomaly Detection. CVPR 2019. paper

Michael Kampffmeyer, Yinbo Chen, Xiaodan Liang, Hao Wang, Yujia Zhang, Eric P. Xing. Rethinking Knowledge Graph Propagation for Zero-Shot Learning. CVPR 2019. paper

Arushi Goel, Keng Teck Ma, Cheston Tan. An End-to-End Network for Generating Social Relationship Graphs. CVPR 2019. paper

Yichao Yan, Qiang Zhang, Bingbing Ni, Wendong Zhang, Minghao Xu, Xiaokang Yang. Learning Context Graph for Person Search. CVPR 2019 paper

Zhongdao Wang, Liang Zheng, Yali Li, Shengjin Wang. Linkage Based Face Clustering via Graph Convolution Network. CVPR 2019 paper

Lei Yang, Xiaohang Zhan, Dapeng Chen, Junjie Yan, Chen Change Loy, Dahua Lin. Learning to Cluster Faces on an Affinity Graph. CVPR 2019 paper

Yao Ma, Suhang Wang, Charu C. Aggarwal, Jiliang Tang. Graph Convolutional Networks with EigenPooling. KDD2019, paper

Wenqi Fan, Yao Ma, Qing Li, Yuan He, Eric Zhao, Jiliang Tang, Dawei Yin. Graph Neural Networks for Social Recommendation. WWW2019, paper

Kim J, Kim T, Kim S, et al. Edge-labeling Graph Neural Network for Few-shot Learning. CVPR 2019. paper

Jessica V. Schrouff, Kai Wohlfahrt, Bruno Marnette, Liam Atkinson. INFERRING JAVASCRIPT TYPES USING GRAPH NEURAL NETWORKS. ICLR 2019. paper

Emanuele Rossi, Federico Monti, Michael Bronstein, Pietro liò. ncRNA Classification with Graph Convolutional Networks. SIGKDD 2019. paper

Wu F, Zhang T, Souza Jr A H, et al. Simplifying Graph Convolutional Networks. ICML 2019. paper.

Junhyun Lee, Inyeop Lee, Jaewoo Kang. Self-Attention Graph Pooling. ICML 2019. paper.

Chiang W L, Liu X, Si S, et al. Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks. SIGKDD 2019. paper.

Namyong Park, Andrey Kan, Xin Luna Dong, Tong Zhao, Christos Faloutsos, Estimating Node Importance in Knowledge Graphs Using Graph Neural Networks. SIGKDD 2019. paper.

Wu S, Tang Y, Zhu Y, et al. Session-based Recommendation with Graph Neural Networks. AAAI 2019. paper.

ArXiv论文

Li Y, Tarlow D, Brockschmidt M, et al. Gated graph sequence neural networks. arXiv 2015. paper

Henaff M, Bruna J, LeCun Y. Deep convolutional networks on graph-structured data, arXiv 2015. paper

Hechtlinger Y, Chakravarti P, Qin J. A generalization of convolutional neural networks to graph-structured data. arXiv 2017. paper

Marcheggiani D, Titov I. Encoding sentences with graph convolutional networks for semantic role labeling. arXiv 2017. paper

Battaglia P W, Hamrick J B, Bapst V, et al. Relational inductive biases, deep learning, and graph networks, arXiv 2018. paper

Verma S, Zhang Z L. Graph Capsule Convolutional Neural Networks. arXiv 2018. paper

Zhang T , Zheng W , Cui Z , et al. Tensor graph convolutional neural network. arXiv 2018. paper

Zou D, Lerman G. Graph Convolutional Neural Networks via Scattering. arXiv 2018. paper

Du J , Zhang S , Wu G , et al. Topology Adaptive Graph Convolutional Networks. arXiv 2018. paper.

Shang C , Liu Q , Chen K S , et al. Edge Attention-based Multi-Relational Graph Convolutional Networks. arXiv 2018. paper.

Scardapane S , Vaerenbergh S V , Comminiello D , et al. Improving Graph Convolutional Networks with Non-Parametric Activation Functions. arXiv 2018. paper.

Wang Y , Sun Y , Liu Z , et al. Dynamic Graph CNN for Learning on Point Clouds. arXiv 2018. paper.

Ryu S , Lim J , Hong S H , et al. Deeply learning molecular structure-property relationships using attention- and gate-augmented graph convolutional network. arXiv 2018. paper.

Cui Z , Henrickson K , Ke R , et al. High-Order Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting. arXiv 2018. paper.

Shchur O , Mumme M , Bojchevski A , et al. Pitfalls of Graph Neural Network Evaluation. arXiv 2018. paper.

Bai Y , Ding H , Bian S , et al. Graph Edit Distance Computation via Graph Neural Networks. arXiv 2018. paper.

Pedro H. C. Avelar, Henrique Lemos, Marcelo O. R. Prates, Luis Lamb, Multitask Learning on Graph Neural Networks - Learning Multiple Graph Centrality Measures with a Unified Network. arXiv 2018. paper.

Matthew Baron, Topology and Prediction Focused Research on Graph Convolutional Neural Networks. arXiv 2018. paper.

Wenting Zhao, Chunyan Xu, Zhen Cui, Tong Zhang, Jiatao Jiang, Zhenyu Zhang, Jian Yang, When Work Matters: Transforming Classical Network Structures to Graph CNN. arXiv 2018. paper.

Xavier Bresson, Thomas Laurent, Residual Gated Graph ConvNets. arXiv 2018. paper.

Kun XuLingfei WuZhiguo WangYansong FengVadim Sheinin, Graph2Seq: Graph to Sequence Learning with Attention-based Neural Networks. arXiv 2018. paper.

Xiaojie GuoLingfei WuLiang Zhao. Deep Graph Translation. arXiv 2018. paper.

Choma, Nicholas, et al. Graph Neural Networks for IceCube Signal Classification. ArXiv 2018. paper.

Tyler Derr, Yao Ma, Jiliang Tang. Signed Graph Convolutional Network ArXiv 2018. paper.

Yawei Luo, Tao Guan, Junqing Yu, Ping Liu, Yi Yang. Every Node Counts: Self-Ensembling Graph Convolutional Networks for Semi-Supervised Learning ArXiv 2018. paper.

Sun K, Koniusz P, Wang J. Fisher-Bures Adversary Graph Convolutional Networks. arXiv 2019. paper.

Kazi A, Burwinkel H, Vivar G, et al. InceptionGCN: Receptive Field Aware Graph Convolutional Network for Disease Prediction. arXiv 2019. paper.

Lemos H, Prates M, Avelar P, et al. Graph Colouring Meets Deep Learning: Effective Graph Neural Network Models for Combinatorial Problems. arXiv 2019. paper.

Diehl F, Brunner T, Le M T, et al. Graph Neural Networks for Modelling Traffic Participant Interaction. arXiv 2019. paper.

Murphy R L, Srinivasan B, Rao V, et al. Relational Pooling for Graph Representations. arXiv 2019. paper.

Zhang W, Shu K, Liu H, et al. Graph Neural Networks for User Identity Linkage. arXiv 2019. paper.

Ruiz L, Gama F, Ribeiro A. Gated Graph Convolutional Recurrent Neural Networks. arXiv 2019. paper.

Phillips S, Daniilidis K. All Graphs Lead to Rome: Learning Geometric and Cycle-Consistent Representations with Graph Convolutional Networks. arXiv 2019. paper.

Hu F, Zhu Y, Wu S, et al. Semi-supervised Node Classification via Hierarchical Graph Convolutional Networks. arXiv 2019. paper.

Deng Z, Dong Y, Zhu J. Batch Virtual Adversarial Training for Graph Convolutional Networks. arXiv 2019. paper.

Chen Z M, Wei X S, Wang P, et al.Multi-Label Image Recognition with Graph Convolutional Networks. arXiv 2019. paper.

Mallea M D G, Meltzer P, Bentley P J. Capsule Neural Networks for Graph Classification using Explicit Tensorial Graph Representations. arXiv 2019. paper.

Peter Meltzer, Marcelo Daniel Gutierrez Mallea and Peter J. Bentley. PiNet: A Permutation Invariant Graph Neural Network for Graph Classification. arXiv 2019. paper.

Padraig Corcoran. Function Space Pooling For Graph Convolutional Networks. arXiv 2019. paper.

Sbastien Lerique, Jacob Levy Abitbol, and Mrton Karsai. Joint embedding of structure and features via graph convolutional networks. arXiv 2019. paper.

GNN相关的一些开源平台

Deep Graph Library（DGL)

DGL由纽约大学、纽约大学上海分校、AWS上海研究所和AWS MXNet科学小组开发和维护GNN平台。

开始时间: 2018.

地址: https://www.dgl.ai/,

github地址:https://github.com/jermainewang/dgl

NGra

NGra是由北京大学和微软亚洲研究院开发和维护一款GNN平台。

开始时间:2018

地址: https://arxiv.org/pdf/1810.08403.pdf

Graph_nets

Graph_nets是由DeepMind, Google Corp开发和维护的.

开始时间:2018

地址: https://github.com/deepmind/graph_nets

Euler

Euler是一款由阿里巴巴旗下的阿里妈妈开源的GNN平台.

开始时间:2019

地址: https://github.com/alibaba/euler

PyTorch Geometric

PyTorch Geometric由德国杜特蒙德大学开发和维护的GNN平台。

开始时间:2019

地址:https://github.com/rusty1s/pytorch_geometric

论文：https://arxiv.org/abs/1903.02428?context=cs.LG

PyTorch-BigGraph（PBG）

PBG是由Facebook人工智能研究开发和维护的GNN平台。

开始时间:2019

地址: https://github.com/facebookresearch/PyTorch-BigGraph

论文：https://arxiv.org/abs/1903.12287